# This file is adapted from ProteinMPNN (https://github.com/dauparas/ProteinMPNN)
# Original implementation by Dauparas et al.
# License: MIT License (https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE)

import argparse
import os.path

from protenix.utils.seed import seed_everything


def main(args):

    import copy
    import glob
    import json
    import os
    import os.path
    import random
    import shutil
    import subprocess
    import sys
    import time
    import warnings

    import numpy as np
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    from torch import optim
    from torch.utils.data import DataLoader
    from torch.utils.data.dataset import Subset, random_split

    from pxdbench.tools.protmpnn.protein_mpnn_utils import (
        ProteinMPNN,
        StructureDataset,
        StructureDatasetPDB,
        _S_to_seq,
        _scores,
        cat_neighbors_nodes,
        gather_edges,
        gather_nodes,
        gather_nodes_t,
        loss_nll,
        loss_smoothed,
        parse_fasta,
        parse_PDB,
        tied_featurize,
    )

    if args.seed:
        seed = args.seed
    else:
        seed = int(np.random.randint(0, high=999, size=1, dtype=int)[0])

    seed_everything(seed, deterministic=True)

    hidden_dim = 128
    num_layers = 3

    if args.path_to_model_weights:
        model_folder_path = args.path_to_model_weights
        if model_folder_path[-1] != "/":
            model_folder_path = model_folder_path + "/"
    else:
        file_path = os.path.realpath(__file__)
        k = file_path.rfind("/")
        if args.ca_only:
            print("Using CA-ProteinMPNN!")
            model_folder_path = file_path[:k] + "/ca_model_weights/"
            if args.use_soluble_model:
                print("WARNING: CA-SolubleMPNN is not available yet")
                sys.exit()
        else:
            if args.use_soluble_model:
                print("Using ProteinMPNN trained on soluble proteins only!")
                model_folder_path = file_path[:k] + "/soluble_model_weights/"
            else:
                model_folder_path = file_path[:k] + "/vanilla_model_weights/"

    checkpoint_path = model_folder_path + f"{args.model_name}.pt"
    folder_for_outputs = args.out_folder

    NUM_BATCHES = args.num_seq_per_target // args.batch_size
    BATCH_COPIES = args.batch_size
    temperatures = [float(item) for item in args.sampling_temp.split()]
    omit_AAs_list = args.omit_AAs
    alphabet = "ACDEFGHIKLMNPQRSTVWYX"
    alphabet_dict = dict(zip(alphabet, range(21)))
    print_all = args.suppress_print == 0
    omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32)
    device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
    if os.path.isfile(args.chain_id_jsonl):
        with open(args.chain_id_jsonl, "r") as json_file:
            json_list = list(json_file)
        for json_str in json_list:
            chain_id_dict = json.loads(json_str)
    else:
        chain_id_dict = None
        if print_all:
            print(40 * "-")
            print("chain_id_jsonl is NOT loaded")

    if os.path.isfile(args.fixed_positions_jsonl):
        with open(args.fixed_positions_jsonl, "r") as json_file:
            json_list = list(json_file)
        for json_str in json_list:
            fixed_positions_dict = json.loads(json_str)
    else:
        if print_all:
            print(40 * "-")
            print("fixed_positions_jsonl is NOT loaded")
        fixed_positions_dict = None

    if os.path.isfile(args.pssm_jsonl):
        with open(args.pssm_jsonl, "r") as json_file:
            json_list = list(json_file)
        pssm_dict = {}
        for json_str in json_list:
            pssm_dict.update(json.loads(json_str))
    else:
        if print_all:
            print(40 * "-")
            print("pssm_jsonl is NOT loaded")
        pssm_dict = None

    if os.path.isfile(args.omit_AA_jsonl):
        with open(args.omit_AA_jsonl, "r") as json_file:
            json_list = list(json_file)
        for json_str in json_list:
            omit_AA_dict = json.loads(json_str)
    else:
        if print_all:
            print(40 * "-")
            print("omit_AA_jsonl is NOT loaded")
        omit_AA_dict = None

    if os.path.isfile(args.bias_AA_jsonl):
        with open(args.bias_AA_jsonl, "r") as json_file:
            json_list = list(json_file)
        for json_str in json_list:
            bias_AA_dict = json.loads(json_str)
    else:
        if print_all:
            print(40 * "-")
            print("bias_AA_jsonl is NOT loaded")
        bias_AA_dict = None

    if os.path.isfile(args.tied_positions_jsonl):
        with open(args.tied_positions_jsonl, "r") as json_file:
            json_list = list(json_file)
        for json_str in json_list:
            tied_positions_dict = json.loads(json_str)
    else:
        if print_all:
            print(40 * "-")
            print("tied_positions_jsonl is NOT loaded")
        tied_positions_dict = None

    if os.path.isfile(args.bias_by_res_jsonl):
        with open(args.bias_by_res_jsonl, "r") as json_file:
            json_list = list(json_file)

        for json_str in json_list:
            bias_by_res_dict = json.loads(json_str)
        if print_all:
            print("bias by residue dictionary is loaded")
    else:
        if print_all:
            print(40 * "-")
            print("bias by residue dictionary is not loaded, or not provided")
        bias_by_res_dict = None

    if print_all:
        print(40 * "-")
    bias_AAs_np = np.zeros(len(alphabet))
    if bias_AA_dict:
        for n, AA in enumerate(alphabet):
            if AA in list(bias_AA_dict.keys()):
                bias_AAs_np[n] = bias_AA_dict[AA]

    if args.pdb_path:
        pdb_dict_list = parse_PDB(args.pdb_path, ca_only=args.ca_only)
        dataset_valid = StructureDatasetPDB(
            pdb_dict_list, truncate=None, max_length=args.max_length
        )
        all_chain_list = [
            item[-1:] for item in list(pdb_dict_list[0]) if item[:9] == "seq_chain"
        ]  # ['A','B', 'C',...]
        if args.pdb_path_chains:
            designed_chain_list = [str(item) for item in args.pdb_path_chains.split()]
        else:
            designed_chain_list = all_chain_list
        fixed_chain_list = [
            letter for letter in all_chain_list if letter not in designed_chain_list
        ]
        chain_id_dict = {}
        chain_id_dict[pdb_dict_list[0]["name"]] = (
            designed_chain_list,
            fixed_chain_list,
        )
    else:
        dataset_valid = StructureDataset(
            args.jsonl_path,
            truncate=None,
            max_length=args.max_length,
            verbose=print_all,
        )

    checkpoint = torch.load(checkpoint_path, map_location=device)
    noise_level_print = checkpoint["noise_level"]
    model = ProteinMPNN(
        ca_only=args.ca_only,
        num_letters=21,
        node_features=hidden_dim,
        edge_features=hidden_dim,
        hidden_dim=hidden_dim,
        num_encoder_layers=num_layers,
        num_decoder_layers=num_layers,
        augment_eps=args.backbone_noise,
        k_neighbors=checkpoint["num_edges"],
    )
    model.to(device)
    model.load_state_dict(checkpoint["model_state_dict"])
    model.eval()

    if print_all:
        print(40 * "-")
        print("Number of edges:", checkpoint["num_edges"])
        print(f"Training noise level: {noise_level_print}A")

    # Build paths for experiment
    base_folder = folder_for_outputs
    if base_folder[-1] != "/":
        base_folder = base_folder + "/"
    if not os.path.exists(base_folder):
        os.makedirs(base_folder)

    if not os.path.exists(base_folder + "seqs"):
        os.makedirs(base_folder + "seqs")

    if args.save_score:
        if not os.path.exists(base_folder + "scores"):
            os.makedirs(base_folder + "scores")

    if args.score_only:
        if not os.path.exists(base_folder + "score_only"):
            os.makedirs(base_folder + "score_only")

    if args.conditional_probs_only:
        if not os.path.exists(base_folder + "conditional_probs_only"):
            os.makedirs(base_folder + "conditional_probs_only")

    if args.unconditional_probs_only:
        if not os.path.exists(base_folder + "unconditional_probs_only"):
            os.makedirs(base_folder + "unconditional_probs_only")

    if args.save_probs:
        if not os.path.exists(base_folder + "probs"):
            os.makedirs(base_folder + "probs")

    # Timing
    start_time = time.time()
    total_residues = 0
    protein_list = []
    total_step = 0
    # Validation epoch
    with torch.no_grad():
        test_sum, test_weights = 0.0, 0.0
        for ix, protein in enumerate(dataset_valid):
            score_list = []
            global_score_list = []
            all_probs_list = []
            all_log_probs_list = []
            S_sample_list = []
            batch_clones = [copy.deepcopy(protein) for i in range(BATCH_COPIES)]
            (
                X,
                S,
                mask,
                lengths,
                chain_M,
                chain_encoding_all,
                chain_list_list,
                visible_list_list,
                masked_list_list,
                masked_chain_length_list_list,
                chain_M_pos,
                omit_AA_mask,
                residue_idx,
                dihedral_mask,
                tied_pos_list_of_lists_list,
                pssm_coef,
                pssm_bias,
                pssm_log_odds_all,
                bias_by_res_all,
                tied_beta,
            ) = tied_featurize(
                batch_clones,
                device,
                chain_id_dict,
                fixed_positions_dict,
                omit_AA_dict,
                tied_positions_dict,
                pssm_dict,
                bias_by_res_dict,
                ca_only=args.ca_only,
            )
            pssm_log_odds_mask = (
                pssm_log_odds_all > args.pssm_threshold
            ).float()  # 1.0 for true, 0.0 for false
            name_ = batch_clones[0]["name"]
            if args.score_only:
                loop_c = 0
                if args.path_to_fasta:
                    fasta_names, fasta_seqs = parse_fasta(
                        args.path_to_fasta, omit=["/"]
                    )
                    loop_c = len(fasta_seqs)
                for fc in range(1 + loop_c):
                    if fc == 0:
                        structure_sequence_score_file = (
                            base_folder
                            + "/score_only/"
                            + batch_clones[0]["name"]
                            + f"_pdb"
                        )
                    else:
                        structure_sequence_score_file = (
                            base_folder
                            + "/score_only/"
                            + batch_clones[0]["name"]
                            + f"_fasta_{fc}"
                        )
                    native_score_list = []
                    global_native_score_list = []
                    if fc > 0:
                        input_seq_length = len(fasta_seqs[fc - 1])
                        S_input = torch.tensor(
                            [alphabet_dict[AA] for AA in fasta_seqs[fc - 1]],
                            device=device,
                        )[None, :].repeat(X.shape[0], 1)
                        S[:, :input_seq_length] = (
                            S_input  # assumes that S and S_input are alphabetically sorted for masked_chains
                        )
                    for j in range(NUM_BATCHES):
                        randn_1 = torch.randn(chain_M.shape, device=X.device)
                        log_probs = model(
                            X,
                            S,
                            mask,
                            chain_M * chain_M_pos,
                            residue_idx,
                            chain_encoding_all,
                            randn_1,
                        )
                        mask_for_loss = mask * chain_M * chain_M_pos
                        scores = _scores(S, log_probs, mask_for_loss)
                        native_score = scores.cpu().data.numpy()
                        native_score_list.append(native_score)
                        global_scores = _scores(S, log_probs, mask)
                        global_native_score = global_scores.cpu().data.numpy()
                        global_native_score_list.append(global_native_score)
                    native_score = np.concatenate(native_score_list, 0)
                    global_native_score = np.concatenate(global_native_score_list, 0)
                    ns_mean = native_score.mean()
                    ns_mean_print = np.format_float_positional(
                        np.float32(ns_mean), unique=False, precision=4
                    )
                    ns_std = native_score.std()
                    ns_std_print = np.format_float_positional(
                        np.float32(ns_std), unique=False, precision=4
                    )

                    global_ns_mean = global_native_score.mean()
                    global_ns_mean_print = np.format_float_positional(
                        np.float32(global_ns_mean), unique=False, precision=4
                    )
                    global_ns_std = global_native_score.std()
                    global_ns_std_print = np.format_float_positional(
                        np.float32(global_ns_std), unique=False, precision=4
                    )

                    ns_sample_size = native_score.shape[0]
                    seq_str = _S_to_seq(S[0,], chain_M[0,])
                    np.savez(
                        structure_sequence_score_file,
                        score=native_score,
                        global_score=global_native_score,
                        S=S[0,].cpu().numpy(),
                        seq_str=seq_str,
                    )
                    if print_all:
                        if fc == 0:
                            print(
                                f"Score for {name_} from PDB, mean: {ns_mean_print}, std: {ns_std_print}, sample size: {ns_sample_size},  global score, mean: {global_ns_mean_print}, std: {global_ns_std_print}, sample size: {ns_sample_size}"
                            )
                        else:
                            print(
                                f"Score for {name_}_{fc} from FASTA, mean: {ns_mean_print}, std: {ns_std_print}, sample size: {ns_sample_size},  global score, mean: {global_ns_mean_print}, std: {global_ns_std_print}, sample size: {ns_sample_size}"
                            )
            elif args.conditional_probs_only:
                if print_all:
                    print(f"Calculating conditional probabilities for {name_}")
                conditional_probs_only_file = (
                    base_folder + "/conditional_probs_only/" + batch_clones[0]["name"]
                )
                log_conditional_probs_list = []
                for j in range(NUM_BATCHES):
                    randn_1 = torch.randn(chain_M.shape, device=X.device)
                    log_conditional_probs = model.conditional_probs(
                        X,
                        S,
                        mask,
                        chain_M * chain_M_pos,
                        residue_idx,
                        chain_encoding_all,
                        randn_1,
                        args.conditional_probs_only_backbone,
                    )
                    log_conditional_probs_list.append(
                        log_conditional_probs.cpu().numpy()
                    )
                concat_log_p = np.concatenate(
                    log_conditional_probs_list, 0
                )  # [B, L, 21]
                mask_out = (chain_M * chain_M_pos * mask)[0,].cpu().numpy()
                np.savez(
                    conditional_probs_only_file,
                    log_p=concat_log_p,
                    S=S[0,].cpu().numpy(),
                    mask=mask[0,].cpu().numpy(),
                    design_mask=mask_out,
                )
            elif args.unconditional_probs_only:
                if print_all:
                    print(
                        f"Calculating sequence unconditional probabilities for {name_}"
                    )
                unconditional_probs_only_file = (
                    base_folder + "/unconditional_probs_only/" + batch_clones[0]["name"]
                )
                log_unconditional_probs_list = []
                for j in range(NUM_BATCHES):
                    log_unconditional_probs = model.unconditional_probs(
                        X, mask, residue_idx, chain_encoding_all
                    )
                    log_unconditional_probs_list.append(
                        log_unconditional_probs.cpu().numpy()
                    )
                concat_log_p = np.concatenate(
                    log_unconditional_probs_list, 0
                )  # [B, L, 21]
                mask_out = (chain_M * chain_M_pos * mask)[0,].cpu().numpy()
                np.savez(
                    unconditional_probs_only_file,
                    log_p=concat_log_p,
                    S=S[0,].cpu().numpy(),
                    mask=mask[0,].cpu().numpy(),
                    design_mask=mask_out,
                )
            else:
                randn_1 = torch.randn(chain_M.shape, device=X.device)
                log_probs = model(
                    X,
                    S,
                    mask,
                    chain_M * chain_M_pos,
                    residue_idx,
                    chain_encoding_all,
                    randn_1,
                )
                mask_for_loss = mask * chain_M * chain_M_pos
                scores = _scores(
                    S, log_probs, mask_for_loss
                )  # score only the redesigned part
                native_score = scores.cpu().data.numpy()
                global_scores = _scores(
                    S, log_probs, mask
                )  # score the whole structure-sequence
                global_native_score = global_scores.cpu().data.numpy()
                # Generate some sequences
                ali_file = base_folder + "/seqs/" + batch_clones[0]["name"] + ".fa"
                score_file = base_folder + "/scores/" + batch_clones[0]["name"] + ".npz"
                probs_file = base_folder + "/probs/" + batch_clones[0]["name"] + ".npz"
                if print_all:
                    print(f"Generating sequences for: {name_}")
                t0 = time.time()
                with open(ali_file, "w") as f:
                    for temp in temperatures:
                        for j in range(NUM_BATCHES):
                            randn_2 = torch.randn(chain_M.shape, device=X.device)
                            if tied_positions_dict == None:
                                sample_dict = model.sample(
                                    X,
                                    randn_2,
                                    S,
                                    chain_M,
                                    chain_encoding_all,
                                    residue_idx,
                                    mask=mask,
                                    temperature=temp,
                                    omit_AAs_np=omit_AAs_np,
                                    bias_AAs_np=bias_AAs_np,
                                    chain_M_pos=chain_M_pos,
                                    omit_AA_mask=omit_AA_mask,
                                    pssm_coef=pssm_coef,
                                    pssm_bias=pssm_bias,
                                    pssm_multi=args.pssm_multi,
                                    pssm_log_odds_flag=bool(args.pssm_log_odds_flag),
                                    pssm_log_odds_mask=pssm_log_odds_mask,
                                    pssm_bias_flag=bool(args.pssm_bias_flag),
                                    bias_by_res=bias_by_res_all,
                                )
                                S_sample = sample_dict["S"]
                            else:
                                sample_dict = model.tied_sample(
                                    X,
                                    randn_2,
                                    S,
                                    chain_M,
                                    chain_encoding_all,
                                    residue_idx,
                                    mask=mask,
                                    temperature=temp,
                                    omit_AAs_np=omit_AAs_np,
                                    bias_AAs_np=bias_AAs_np,
                                    chain_M_pos=chain_M_pos,
                                    omit_AA_mask=omit_AA_mask,
                                    pssm_coef=pssm_coef,
                                    pssm_bias=pssm_bias,
                                    pssm_multi=args.pssm_multi,
                                    pssm_log_odds_flag=bool(args.pssm_log_odds_flag),
                                    pssm_log_odds_mask=pssm_log_odds_mask,
                                    pssm_bias_flag=bool(args.pssm_bias_flag),
                                    tied_pos=tied_pos_list_of_lists_list[0],
                                    tied_beta=tied_beta,
                                    bias_by_res=bias_by_res_all,
                                )
                                # Compute scores
                                S_sample = sample_dict["S"]
                            log_probs = model(
                                X,
                                S_sample,
                                mask,
                                chain_M * chain_M_pos,
                                residue_idx,
                                chain_encoding_all,
                                randn_2,
                                use_input_decoding_order=True,
                                decoding_order=sample_dict["decoding_order"],
                            )
                            mask_for_loss = mask * chain_M * chain_M_pos
                            scores = _scores(S_sample, log_probs, mask_for_loss)
                            scores = scores.cpu().data.numpy()

                            global_scores = _scores(
                                S_sample, log_probs, mask
                            )  # score the whole structure-sequence
                            global_scores = global_scores.cpu().data.numpy()

                            all_probs_list.append(
                                sample_dict["probs"].cpu().data.numpy()
                            )
                            all_log_probs_list.append(log_probs.cpu().data.numpy())
                            S_sample_list.append(S_sample.cpu().data.numpy())
                            for b_ix in range(BATCH_COPIES):
                                masked_chain_length_list = (
                                    masked_chain_length_list_list[b_ix]
                                )
                                masked_list = masked_list_list[b_ix]
                                seq_recovery_rate = torch.sum(
                                    torch.sum(
                                        torch.nn.functional.one_hot(S[b_ix], 21)
                                        * torch.nn.functional.one_hot(
                                            S_sample[b_ix], 21
                                        ),
                                        axis=-1,
                                    )
                                    * mask_for_loss[b_ix]
                                ) / torch.sum(mask_for_loss[b_ix])
                                seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix])
                                score = scores[b_ix]
                                score_list.append(score)
                                global_score = global_scores[b_ix]
                                global_score_list.append(global_score)
                                native_seq = _S_to_seq(S[b_ix], chain_M[b_ix])
                                if b_ix == 0 and j == 0 and temp == temperatures[0]:
                                    start = 0
                                    end = 0
                                    list_of_AAs = []
                                    for mask_l in masked_chain_length_list:
                                        end += mask_l
                                        list_of_AAs.append(native_seq[start:end])
                                        start = end
                                    native_seq = "".join(
                                        list(
                                            np.array(list_of_AAs)[
                                                np.argsort(masked_list)
                                            ]
                                        )
                                    )
                                    l0 = 0
                                    for mc_length in list(
                                        np.array(masked_chain_length_list)[
                                            np.argsort(masked_list)
                                        ]
                                    )[:-1]:
                                        l0 += mc_length
                                        native_seq = (
                                            native_seq[:l0] + "/" + native_seq[l0:]
                                        )
                                        l0 += 1
                                    sorted_masked_chain_letters = np.argsort(
                                        masked_list_list[0]
                                    )
                                    print_masked_chains = [
                                        masked_list_list[0][i]
                                        for i in sorted_masked_chain_letters
                                    ]
                                    sorted_visible_chain_letters = np.argsort(
                                        visible_list_list[0]
                                    )
                                    print_visible_chains = [
                                        visible_list_list[0][i]
                                        for i in sorted_visible_chain_letters
                                    ]
                                    native_score_print = np.format_float_positional(
                                        np.float32(native_score.mean()),
                                        unique=False,
                                        precision=4,
                                    )
                                    global_native_score_print = (
                                        np.format_float_positional(
                                            np.float32(global_native_score.mean()),
                                            unique=False,
                                            precision=4,
                                        )
                                    )
                                    script_dir = os.path.dirname(
                                        os.path.realpath(__file__)
                                    )
                                    try:
                                        commit_str = (
                                            subprocess.check_output(
                                                f"git --git-dir {script_dir}/.git rev-parse HEAD",
                                                shell=True,
                                                stderr=subprocess.DEVNULL,
                                            )
                                            .decode()
                                            .strip()
                                        )
                                    except subprocess.CalledProcessError:
                                        commit_str = "unknown"
                                    if args.ca_only:
                                        print_model_name = "CA_model_name"
                                    else:
                                        print_model_name = "model_name"
                                    f.write(
                                        ">{}, score={}, global_score={}, fixed_chains={}, designed_chains={}, {}={}, git_hash={}, seed={}\n{}\n".format(
                                            name_,
                                            native_score_print,
                                            global_native_score_print,
                                            print_visible_chains,
                                            print_masked_chains,
                                            print_model_name,
                                            args.model_name,
                                            commit_str,
                                            seed,
                                            native_seq,
                                        )
                                    )  # write the native sequence
                                start = 0
                                end = 0
                                list_of_AAs = []
                                for mask_l in masked_chain_length_list:
                                    end += mask_l
                                    list_of_AAs.append(seq[start:end])
                                    start = end

                                seq = "".join(
                                    list(np.array(list_of_AAs)[np.argsort(masked_list)])
                                )
                                l0 = 0
                                for mc_length in list(
                                    np.array(masked_chain_length_list)[
                                        np.argsort(masked_list)
                                    ]
                                )[:-1]:
                                    l0 += mc_length
                                    seq = seq[:l0] + "/" + seq[l0:]
                                    l0 += 1
                                score_print = np.format_float_positional(
                                    np.float32(score), unique=False, precision=4
                                )
                                global_score_print = np.format_float_positional(
                                    np.float32(global_score), unique=False, precision=4
                                )
                                seq_rec_print = np.format_float_positional(
                                    np.float32(
                                        seq_recovery_rate.detach().cpu().numpy()
                                    ),
                                    unique=False,
                                    precision=4,
                                )
                                sample_number = j * BATCH_COPIES + b_ix + 1
                                f.write(
                                    ">T={}, sample={}, score={}, global_score={}, seq_recovery={}\n{}\n".format(
                                        temp,
                                        sample_number,
                                        score_print,
                                        global_score_print,
                                        seq_rec_print,
                                        seq,
                                    )
                                )  # write generated sequence
                if args.save_score:
                    np.savez(
                        score_file,
                        score=np.array(score_list, np.float32),
                        global_score=np.array(global_score_list, np.float32),
                    )
                if args.save_probs:
                    all_probs_concat = np.concatenate(all_probs_list)
                    all_log_probs_concat = np.concatenate(all_log_probs_list)
                    S_sample_concat = np.concatenate(S_sample_list)
                    np.savez(
                        probs_file,
                        probs=np.array(all_probs_concat, np.float32),
                        log_probs=np.array(all_log_probs_concat, np.float32),
                        S=np.array(S_sample_concat, np.int32),
                        mask=mask_for_loss.cpu().data.numpy(),
                        chain_order=chain_list_list,
                    )
                t1 = time.time()
                dt = round(float(t1 - t0), 4)
                num_seqs = len(temperatures) * NUM_BATCHES * BATCH_COPIES
                total_length = X.shape[1]
                if print_all:
                    print(
                        f"{num_seqs} sequences of length {total_length} generated in {dt} seconds"
                    )


if __name__ == "__main__":
    argparser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    argparser.add_argument(
        "--suppress_print", type=int, default=0, help="0 for False, 1 for True"
    )

    argparser.add_argument(
        "--ca_only",
        action="store_true",
        default=False,
        help="Parse CA-only structures and use CA-only models (default: false)",
    )
    argparser.add_argument(
        "--path_to_model_weights",
        type=str,
        default="",
        help="Path to model weights folder;",
    )
    argparser.add_argument(
        "--model_name",
        type=str,
        default="v_48_020",
        help="ProteinMPNN model name: v_48_002, v_48_010, v_48_020, v_48_030; v_48_010=version with 48 edges 0.10A noise",
    )
    argparser.add_argument(
        "--use_soluble_model",
        action="store_true",
        default=False,
        help="Flag to load ProteinMPNN weights trained on soluble proteins only.",
    )

    argparser.add_argument(
        "--seed",
        type=int,
        default=0,
        help="If set to 0 then a random seed will be picked;",
    )

    argparser.add_argument(
        "--save_score",
        type=int,
        default=0,
        help="0 for False, 1 for True; save score=-log_prob to npy files",
    )
    argparser.add_argument(
        "--save_probs",
        type=int,
        default=0,
        help="0 for False, 1 for True; save MPNN predicted probabilites per position",
    )

    argparser.add_argument(
        "--score_only",
        type=int,
        default=0,
        help="0 for False, 1 for True; score input backbone-sequence pairs",
    )
    argparser.add_argument(
        "--path_to_fasta",
        type=str,
        default="",
        help="score provided input sequence in a fasta format; e.g. GGGGGG/PPPPS/WWW for chains A, B, C sorted alphabetically and separated by /",
    )

    argparser.add_argument(
        "--conditional_probs_only",
        type=int,
        default=0,
        help="0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone)",
    )
    argparser.add_argument(
        "--conditional_probs_only_backbone",
        type=int,
        default=0,
        help="0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone)",
    )
    argparser.add_argument(
        "--unconditional_probs_only",
        type=int,
        default=0,
        help="0 for False, 1 for True; output unconditional probabilities p(s_i given backbone) in one forward pass",
    )

    argparser.add_argument(
        "--backbone_noise",
        type=float,
        default=0.00,
        help="Standard deviation of Gaussian noise to add to backbone atoms",
    )
    argparser.add_argument(
        "--num_seq_per_target",
        type=int,
        default=1,
        help="Number of sequences to generate per target",
    )
    argparser.add_argument(
        "--batch_size",
        type=int,
        default=1,
        help="Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory",
    )
    argparser.add_argument(
        "--max_length", type=int, default=200000, help="Max sequence length"
    )
    argparser.add_argument(
        "--sampling_temp",
        type=str,
        default="0.1",
        help="A string of temperatures, 0.2 0.25 0.5. Sampling temperature for amino acids. Suggested values 0.1, 0.15, 0.2, 0.25, 0.3. Higher values will lead to more diversity.",
    )

    argparser.add_argument(
        "--out_folder",
        type=str,
        help="Path to a folder to output sequences, e.g. /home/out/",
    )
    argparser.add_argument(
        "--pdb_path", type=str, default="", help="Path to a single PDB to be designed"
    )
    argparser.add_argument(
        "--pdb_path_chains",
        type=str,
        default="",
        help="Define which chains need to be designed for a single PDB ",
    )
    argparser.add_argument(
        "--jsonl_path", type=str, help="Path to a folder with parsed pdb into jsonl"
    )
    argparser.add_argument(
        "--chain_id_jsonl",
        type=str,
        default="",
        help="Path to a dictionary specifying which chains need to be designed and which ones are fixed, if not specied all chains will be designed.",
    )
    argparser.add_argument(
        "--fixed_positions_jsonl",
        type=str,
        default="",
        help="Path to a dictionary with fixed positions",
    )
    argparser.add_argument(
        "--omit_AAs",
        type=list,
        default="X",
        help="Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.",
    )
    argparser.add_argument(
        "--bias_AA_jsonl",
        type=str,
        default="",
        help="Path to a dictionary which specifies AA composion bias if neededi, e.g. {A: -1.1, F: 0.7} would make A less likely and F more likely.",
    )

    argparser.add_argument(
        "--bias_by_res_jsonl",
        default="",
        help="Path to dictionary with per position bias.",
    )
    argparser.add_argument(
        "--omit_AA_jsonl",
        type=str,
        default="",
        help="Path to a dictionary which specifies which amino acids need to be omited from design at specific chain indices",
    )
    argparser.add_argument(
        "--pssm_jsonl", type=str, default="", help="Path to a dictionary with pssm"
    )
    argparser.add_argument(
        "--pssm_multi",
        type=float,
        default=0.0,
        help="A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions",
    )
    argparser.add_argument(
        "--pssm_threshold",
        type=float,
        default=0.0,
        help="A value between -inf + inf to restric per position AAs",
    )
    argparser.add_argument(
        "--pssm_log_odds_flag", type=int, default=0, help="0 for False, 1 for True"
    )
    argparser.add_argument(
        "--pssm_bias_flag", type=int, default=0, help="0 for False, 1 for True"
    )

    argparser.add_argument(
        "--tied_positions_jsonl",
        type=str,
        default="",
        help="Path to a dictionary with tied positions",
    )

    args = argparser.parse_args()
    main(args)
